Snap ATAC data processing
library(ggpubr)
Loading required package: magrittr
Attaching package: ‘magrittr’
The following object is masked from ‘package:purrr’:
set_names
The following object is masked from ‘package:tidyr’:
extract
Attaching package: ‘ggpubr’
The following object is masked from ‘package:cowplot’:
get_legendf74.rna.seu@meta.data <- f74.rna.seu@meta.data %>%
rownames_to_column("cell") %>%
left_join(annotation.df) %>%
column_to_rownames("cell")
Joining, by = "cell"First I have made the snap file from the cellranger fragments.tsv file. Script is multiOmic_benchmark/preprocess/fragments2snap.sh.
Following integration vignette
Barcode selection
Filtering based on number of reads per cell and ratio of fragments that are within promoters
barcode.ls = lapply(seq(snap.files), function(i){
barcodes = barcode.ls[[i]];
idx = which(
barcodes$logUMI >= cutoff.logUMI.low[i] &
barcodes$logUMI <= cutoff.logUMI.high[i] &
barcodes$promoter_ratio >= cutoff.FRIP.low[i] &
barcodes$promoter_ratio <= cutoff.FRIP.high[i]
);
barcodes[idx,]
});
x.sp.ls = lapply(seq(snap.files), function(i){
barcodes = barcode.ls[[i]];
x.sp = x.sp.ls[[i]];
barcode.shared = intersect(x.sp@barcode, barcodes$barcode);
x.sp = x.sp[match(barcode.shared, x.sp@barcode),];
barcodes = barcodes[match(barcode.shared, barcodes$barcode),];
x.sp@metaData = barcodes;
x.sp
})
names(x.sp.ls) = sample.names;
x.sp = Reduce(snapRbind, x.sp.ls);
x.sp@metaData["sample"] = x.sp@sample;
x.spnumber of barcodes: 6043
number of bins: 0
number of genes: 0
number of peaks: 0
number of motifs: 0Add cell-by-bin matrix
x.sp = addBmatToSnap(x.sp, bin.size = 5000)Epoch: reading cell-bin count matrix session ...Binarize matrix
Some items in the count matrix have abnormally high coverage perhaps due to the alignment errors. Therefore, we next remove top 0.1% items in the count matrix and then convert the remaining non-zero values to 1.
x.sp = makeBinary(x.sp, mat="bmat")
x.spnumber of barcodes: 6043
number of bins: 651791
number of genes: 0
number of peaks: 0
number of motifs: 0Filter bins
Filter out bins overlapping w ENCODE blacklist
black_list = read.table("~/annotations/hg38.blacklist.bed.gz")
black_list.gr = GRanges(
black_list[,1],
IRanges(black_list[,2], black_list[,3])
);
idy = queryHits(
findOverlaps(x.sp@feature, black_list.gr)
);
if(length(idy) > 0){
x.sp = x.sp[,-idy, mat="bmat"];
};
x.spnumber of barcodes: 6043
number of bins: 651765
number of genes: 0
number of peaks: 0
number of motifs: 0Exclude bad chromosomes
chr.exclude = seqlevels(x.sp@feature)[grep("random|chrM", seqlevels(x.sp@feature))]
idy = grep(paste(chr.exclude, collapse="|"), x.sp@feature)
if(length(idy) > 0){
x.sp = x.sp[,-idy, mat="bmat"]
}
x.spnumber of barcodes: 6043
number of bins: 650342
number of genes: 0
number of peaks: 0
number of motifs: 0remove the top 5% bins that overlap with invariant features such as the house keeping gene promoters
bin.cov = log10(Matrix::colSums(x.sp@bmat)+1)
# bin.cov = Matrix::colSums(x.sp@bmat)
hist(
bin.cov[bin.cov > 0],
xlab="log10(bin cov)",
main="log10(Bin Cov)",
col="lightblue",
# xlim=c(0, 5),
breaks=100
);
bin.cutoff = quantile(bin.cov[bin.cov > 0], 0.95)
idy = which(bin.cov <= bin.cutoff & bin.cov > 0)
x.sp = x.sp[, idy, mat="bmat"];
x.spnumber of barcodes: 6043
number of bins: 539584
number of genes: 0
number of peaks: 0
number of motifs: 0Remove any cells of bin coverage less than 1,000. The rational behind this is that some cells may have high number of unique fragments but end up with low bin coverage after filtering. This step is optional but highly recommanded.
idx = which(Matrix::rowSums(x.sp@bmat) > 1000);
x.sp = x.sp[idx,];
x.spnumber of barcodes: 5793
number of bins: 539584
number of genes: 0
number of peaks: 0
number of motifs: 0Dimensionality reduction
Uses diffusion map algorithm w sampling technique to make it fast.
## Sample 100 cells as landmarks
row.covs.dens <- density(
x = x.sp@metaData[,"logUMI"],
bw = 'nrd', adjust = 1
)
sampling_prob <- 1 / (approx(x = row.covs.dens$x, y = row.covs.dens$y, xout = x.sp@metaData[,"logUMI"])$y + .Machine$double.eps);
set.seed(1)
idx.landmark.ds <- sort(sample(x = seq(nrow(x.sp)), size = 1000, prob = sampling_prob))
## Split between landmark and query cells
x.landmark.sp = x.sp[idx.landmark.ds,];
x.query.sp = x.sp[-idx.landmark.ds,];
## Run diffusion map on landmark
x.landmark.sp = runDiffusionMaps(
obj= x.landmark.sp,
input.mat="bmat",
num.eigs=50
);Epoch: checking the inputs ...
Epoch: computing jaccard similarity matrix ...
Epoch: fitting regression model ...
Epoch: performing normalization ...
Epoch: computing eigen decomposition ...
Epoch: Donex.landmark.sp@metaData$landmark = 1;
## Project query cells
x.query.sp = runDiffusionMapsExtension(
obj1=x.landmark.sp,
obj2=x.query.sp,
input.mat="bmat"
)Epoch: checking the inputs ...
Epoch: computing jaccard similarity matrix ...
Epoch: performing normalization ...
Epoch: projecting query cells to the reference ...
Epoch: Donex.query.sp@metaData$landmark = 0;
## Combine
x.sp = snapRbind(x.landmark.sp, x.query.sp);'rBind' is deprecated.
Since R version 3.2.0, base's rbind() should work fine with S4 objectsx.sp = x.sp[order(x.sp@metaData[,"sample"])]; #IMPORTANTTo determine significant diffusion components: > We use an ad hoc method by simply looking at a pairwise plot and select the number of eigen vectors that the scatter plot starts looking like a blob. In the below example, we choose the first 15 eigen vectors.
plotDimReductPW(
obj=x.sp,
eigs.dims=1:50,
point.size=0.3,
point.color="grey",
point.shape=19,
point.alpha=0.6,
down.sample=5000,
pdf.file.name=NULL,
pdf.height=7,
pdf.width=7
);
Clustering and visualization
x.sp=runCluster(
obj=x.sp,
tmp.folder=tempdir(),
louvain.lib="leiden",
seed.use=10,
resolution=0.7
);
Epoch: checking input parameters
Epoch: finding clusters using leidenVisualization
Make gene matrix
x.sp <- readRDS( "~/my_data/cellranger-atac110_count_30439_WSSS8038360_GRCh38-1_1_0.snapATAC.RDS")
Loading required package: SnapATAC
Loading required package: rhdf5
Attaching package: ‘SnapATAC’
The following object is masked from ‘package:liger’:
plotGeneRepeat visualization on gmat
FeaturePlot(f74.seu, features = c('PTPRC','CD4','CD8A','CD8B','CD79A','FOXN1','EPCAM','PDGFRA','GNG4', 'FOXP3','RAG1','RAG2','NKG7','CCR7'), reduction = "umap.snap")
All cells have the same value (0) of CD79A.All cells have the same value (0) of RAG2.All cells have the same value (0) of NKG7.All cells have the same value (0) of CCR7.DimHeatmap(f74.seu, dims = 1:6, balanced = TRUE,ncol = 1)
31 Oct 2019 09:28:40 [rsession-] CLIENT EXCEPTION (rsession-jovyan): (TypeError) : Cannot read property 'a' of null;
org/rstudio/studio/client/workbench/views/source/editors/text/ChunkPlotPage.java#52::execute
org/rstudio/studio/client/workbench/views/source/editors/text/ChunkPlotWidget.java#49::onBrowserEvent
com/google/gwt/user/client/DOM.java#1414::dispatchEvent
com/google/gwt/user/client/impl/DOMImplStandard.java#312::dispatchEvent
com/google/gwt/user/client/impl/DOMImplStandard.java#334::dispatchUnhandledEvent
com/google/gwt/core/client/impl/Impl.java#244::apply
com/google/gwt/core/client/impl/Impl.java#283::entry0
rstudio-0.js#-1::eval
Client-ID: 7beb3877-891d-4e16-98d5-f8cca1f72d5d
User-Agent: Mozilla/5.0 (Macintosh Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36
Differential analysis
DAGs_cl <- FindAllMarkers(f74.seu, min.pct = 0.2, logfc.threshold = 0.3)Calculating cluster 1
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|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~02m 24s
|++ | 2 % ~02m 23s
|++ | 3 % ~02m 20s
|+++ | 4 % ~02m 18s
|+++ | 5 % ~02m 15s
|++++ | 6 % ~02m 12s
|++++ | 7 % ~02m 29s
|+++++ | 8 % ~02m 24s
|+++++ | 9 % ~02m 20s
|++++++ | 10% ~02m 17s
|++++++ | 11% ~02m 14s
|+++++++ | 12% ~02m 11s
|+++++++ | 13% ~02m 09s
|++++++++ | 14% ~02m 06s
|++++++++ | 15% ~02m 04s
|+++++++++ | 16% ~02m 02s
|+++++++++ | 17% ~01m 60s
|++++++++++ | 18% ~01m 58s
|++++++++++ | 19% ~01m 56s
|+++++++++++ | 20% ~01m 54s
|+++++++++++ | 21% ~01m 53s
|++++++++++++ | 22% ~01m 51s
|++++++++++++ | 23% ~01m 49s
|+++++++++++++ | 24% ~01m 47s
|+++++++++++++ | 25% ~01m 45s
|++++++++++++++ | 26% ~01m 43s
|++++++++++++++ | 27% ~01m 42s
|+++++++++++++++ | 28% ~01m 40s
|+++++++++++++++ | 29% ~01m 39s
|++++++++++++++++ | 30% ~01m 37s
|++++++++++++++++ | 31% ~01m 35s
|+++++++++++++++++ | 32% ~01m 34s
|+++++++++++++++++ | 33% ~01m 32s
|++++++++++++++++++ | 34% ~01m 31s
|++++++++++++++++++ | 35% ~01m 29s
|+++++++++++++++++++ | 36% ~01m 31s
|+++++++++++++++++++ | 37% ~01m 29s
|++++++++++++++++++++ | 38% ~01m 28s
|++++++++++++++++++++ | 39% ~01m 26s
|+++++++++++++++++++++ | 40% ~01m 25s
|+++++++++++++++++++++ | 41% ~01m 23s
|++++++++++++++++++++++ | 42% ~01m 22s
|++++++++++++++++++++++ | 43% ~01m 20s
|+++++++++++++++++++++++ | 44% ~01m 19s
|+++++++++++++++++++++++ | 45% ~01m 17s
|++++++++++++++++++++++++ | 46% ~01m 15s
|++++++++++++++++++++++++ | 47% ~01m 14s
|+++++++++++++++++++++++++ | 48% ~01m 12s
|+++++++++++++++++++++++++ | 49% ~01m 11s
|++++++++++++++++++++++++++ | 51% ~01m 09s
|++++++++++++++++++++++++++ | 52% ~01m 08s
|+++++++++++++++++++++++++++ | 53% ~01m 06s
|+++++++++++++++++++++++++++ | 54% ~01m 05s
|++++++++++++++++++++++++++++ | 55% ~01m 03s
|++++++++++++++++++++++++++++ | 56% ~01m 02s
|+++++++++++++++++++++++++++++ | 57% ~01m 00s
|+++++++++++++++++++++++++++++ | 58% ~59s
|++++++++++++++++++++++++++++++ | 59% ~01m 04s
|++++++++++++++++++++++++++++++ | 60% ~01m 03s
|+++++++++++++++++++++++++++++++ | 61% ~01m 01s
|+++++++++++++++++++++++++++++++ | 62% ~59s
|++++++++++++++++++++++++++++++++ | 63% ~57s
|++++++++++++++++++++++++++++++++ | 64% ~56s
|+++++++++++++++++++++++++++++++++ | 65% ~54s
|+++++++++++++++++++++++++++++++++ | 66% ~52s
|++++++++++++++++++++++++++++++++++ | 67% ~51s
|++++++++++++++++++++++++++++++++++ | 68% ~49s
|+++++++++++++++++++++++++++++++++++ | 69% ~47s
|+++++++++++++++++++++++++++++++++++ | 70% ~46s
|++++++++++++++++++++++++++++++++++++ | 71% ~44s
|++++++++++++++++++++++++++++++++++++ | 72% ~42s
|+++++++++++++++++++++++++++++++++++++ | 73% ~41s
|+++++++++++++++++++++++++++++++++++++ | 74% ~39s
|++++++++++++++++++++++++++++++++++++++ | 75% ~38s
|++++++++++++++++++++++++++++++++++++++ | 76% ~36s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~34s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~33s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~31s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~30s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~28s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~27s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~25s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~24s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~22s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~21s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~19s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~15s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 22sCalculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~01m 34s
|++ | 2 % ~01m 32s
|++ | 3 % ~01m 34s
|+++ | 4 % ~01m 32s
|+++ | 5 % ~01m 59s
|++++ | 6 % ~01m 53s
|++++ | 7 % ~01m 48s
|+++++ | 8 % ~01m 45s
|+++++ | 9 % ~01m 41s
|++++++ | 10% ~01m 38s
|++++++ | 11% ~01m 36s
|+++++++ | 12% ~01m 34s
|+++++++ | 13% ~01m 32s
|++++++++ | 14% ~01m 30s
|++++++++ | 15% ~01m 28s
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|++++++++++++ | 22% ~01m 19s
|++++++++++++ | 23% ~01m 17s
|+++++++++++++ | 24% ~01m 16s
|+++++++++++++ | 26% ~01m 15s
|++++++++++++++ | 27% ~01m 14s
|++++++++++++++ | 28% ~01m 12s
|+++++++++++++++ | 29% ~01m 11s
|+++++++++++++++ | 30% ~01m 10s
|++++++++++++++++ | 31% ~01m 09s
|++++++++++++++++ | 32% ~01m 08s
|+++++++++++++++++ | 33% ~01m 07s
|+++++++++++++++++ | 34% ~01m 06s
|++++++++++++++++++ | 35% ~01m 05s
|++++++++++++++++++ | 36% ~01m 04s
|+++++++++++++++++++ | 37% ~01m 03s
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|++++++++++++++++++++ | 39% ~01m 00s
|++++++++++++++++++++ | 40% ~59s
|+++++++++++++++++++++ | 41% ~58s
|+++++++++++++++++++++ | 42% ~57s
|++++++++++++++++++++++ | 43% ~56s
|++++++++++++++++++++++ | 44% ~55s
|+++++++++++++++++++++++ | 45% ~54s
|+++++++++++++++++++++++ | 46% ~53s
|++++++++++++++++++++++++ | 47% ~52s
|++++++++++++++++++++++++ | 48% ~51s
|+++++++++++++++++++++++++ | 49% ~50s
|+++++++++++++++++++++++++ | 50% ~49s
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|++++++++++++++++++++++++++++ | 55% ~44s
|+++++++++++++++++++++++++++++ | 56% ~43s
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|++++++++++++++++++++++++++++++ | 58% ~41s
|++++++++++++++++++++++++++++++ | 59% ~40s
|+++++++++++++++++++++++++++++++ | 60% ~39s
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|++++++++++++++++++++++++++++++++ | 62% ~37s
|++++++++++++++++++++++++++++++++ | 63% ~36s
|+++++++++++++++++++++++++++++++++ | 64% ~35s
|+++++++++++++++++++++++++++++++++ | 65% ~34s
|++++++++++++++++++++++++++++++++++ | 66% ~33s
|++++++++++++++++++++++++++++++++++ | 67% ~33s
|+++++++++++++++++++++++++++++++++++ | 68% ~32s
|+++++++++++++++++++++++++++++++++++ | 69% ~31s
|++++++++++++++++++++++++++++++++++++ | 70% ~30s
|++++++++++++++++++++++++++++++++++++ | 71% ~28s
|+++++++++++++++++++++++++++++++++++++ | 72% ~27s
|+++++++++++++++++++++++++++++++++++++ | 73% ~26s
|++++++++++++++++++++++++++++++++++++++ | 74% ~25s
|++++++++++++++++++++++++++++++++++++++ | 76% ~24s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~23s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~22s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~21s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~20s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~19s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~18s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~17s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~11s
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|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~07s
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 39sCalculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~01m 35s
|+ | 2 % ~01m 35s
|++ | 3 % ~01m 33s
|++ | 4 % ~01m 34s
|+++ | 5 % ~01m 33s
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|++++++++ | 15% ~01m 21s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 39sCalculating cluster 10
| | 0 % ~calculating
|+ | 1 % ~02m 55s
|++ | 2 % ~02m 47s
|++ | 3 % ~02m 43s
|+++ | 4 % ~02m 40s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 57sDAGs_cl <-
DAGs_cl %>%
mutate(positive=avg_logFC > 0)top10_markers <- DAGs_cl %>%
filter(positive) %>%
group_by(cluster) %>%
arrange(p_val_adj) %>%
top_n(10) %>%
pull(gene) %>%
unique()Selecting by positivetop10_markers <- DAGs_cl %>%
filter(positive) %>%
group_by(cluster) %>%
# arrange(p_val_adj) %>%
top_n(10,wt = - p_val_adj) %>%
pull(gene)
DotPlot(f74.seu, features = top10_markers) +
coord_flip()
Expression of markers in RNA data
map(1:10, ~ plot_cluster_marker_ATACvsRNA(.x))
All cells have the same value (0) of MIR4300HG.All cells have the same value (0) of NOS1.All cells have the same value (0) of RPH3A.Cannot convert object of class listggarrange into a grob.All cells have the same value (0) of C5orf66-AS2.All cells have the same value (0) of CYP3A43.[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
Differential analysis only between the putative T-cell clusters
Only between cluster 1 and 2
Embedding on most variable genes in ATAC (not in gene expression)
Save markers
Thoughts
- There seems to be a correspondance for gene expression and accessibility in many “accessibility markers” (see Cluster 6) –> maybe integration would work better if I don’t take only the most variable features in the RNA data.
---
title: "Snap ATAC data processing"
output: html_notebook
---

```{r}
suppressPackageStartupMessages({
  library(SnapATAC)
  library(leiden)
  library(umap)
  library(GenomicRanges)
  library(zoo)
  library(tidyverse)
  library(cowplot)
  library(ggpubr)
})
source("~/multiOmic_benchmark/preprocess/selectFeatures.R")
```
```{r}
## Load and embed RNA data
f74.sce.list <- readRDS("~/my_data/integrated_thymus/F74_SCElist_20191017.RDS")
f74.rna <- f74.sce.list$RNA

f74.rna.seu <- as.Seurat(f74.rna, verbose=FALSE)
integrate_features <- HVG_Seurat(f74.rna, nfeatures = 3000)
VariableFeatures(f74.rna.seu) <- integrate_features
f74.rna.seu <- ScaleData(f74.rna.seu, do.center = TRUE, verbose=FALSE)
f74.rna.seu <- RunPCA(f74.rna.seu) 
f74.rna.seu <- RunUMAP(f74.rna.seu, reduction = "pca", dims=1:50) 

## Add cell type annotation
annotation.df <- read.csv("~/my_data/F74_RNA_obs.csv")
annotation.df <- annotation.df %>%
  mutate(cell=str_remove(as.character(X), "F74_1_") %>% str_c(ifelse(batch==0,'_1', "_2"))) 

f74.rna.seu@meta.data <- f74.rna.seu@meta.data %>%
  rownames_to_column("cell") %>%
  left_join(annotation.df) %>%
  column_to_rownames("cell")

```


First I have made the snap file from the cellranger `fragments.tsv` file. Script is `multiOmic_benchmark/preprocess/fragments2snap.sh`.

Following [integration vignette](https://github.com/r3fang/SnapATAC/blob/master/examples/10X_PBMC_15K/README.md) 

### Barcode selection
Filtering based on number of reads per cell and ratio of fragments that are within promoters
```{r}
snap.files <- "~/my_data/cellranger-atac110_count_30439_WSSS8038360_GRCh38-1_1_0.snap"
sample.names <- "F74"
barcode.files <- '~/my_data/singlecell.csv'

x.sp.ls <- list(createSnap(snap.files, sample.names))
names(x.sp.ls) = sample.names
x.sp.ls

barcode.ls = lapply(seq(snap.files), function(i){
    barcodes = read.csv(
        barcode.files[i], 
        head=TRUE
    );
    # remove NO BAROCDE line
    barcodes = barcodes[2:nrow(barcodes),];
    barcodes$logUMI = log10(barcodes$passed_filters + 1);
    barcodes$promoter_ratio = (barcodes$promoter_region_fragments+1) / (barcodes$passed_filters + 1);
    barcodes
  })

plots = lapply(seq(snap.files), function(i){
    p1 = ggplot(
        barcode.ls[[i]], 
        aes(x=logUMI, y=promoter_ratio)) + 
        geom_point(size=0.3, col="grey") +
        theme_classic()	+
        ggtitle(sample.names[[i]]) +
        ylim(0, 1) + xlim(0, 6) + 
        labs(x = "log10(counts)", y="promoter ratio")
        p1
    })

## Select and viz cutoffs
cutoff.logUMI.low = 3.3
cutoff.logUMI.high = 4.8
cutoff.FRIP.low = 0.25
cutoff.FRIP.high = 0.65


plots[[1]] + 
  geom_vline(xintercept = c(cutoff.logUMI.low[1],cutoff.logUMI.high[1]), linetype=2) +
  geom_hline(yintercept = c(cutoff.FRIP.low[1],cutoff.FRIP.high[1]), linetype=2)
```
```{r}
barcode.ls = lapply(seq(snap.files), function(i){
  barcodes = barcode.ls[[i]];
  idx = which(
      barcodes$logUMI >= cutoff.logUMI.low[i] & 
      barcodes$logUMI <= cutoff.logUMI.high[i] & 
      barcodes$promoter_ratio >= cutoff.FRIP.low[i] &
      barcodes$promoter_ratio <= cutoff.FRIP.high[i]
  );
  barcodes[idx,]
});
x.sp.ls = lapply(seq(snap.files), function(i){
  barcodes = barcode.ls[[i]];
  x.sp = x.sp.ls[[i]];
  barcode.shared = intersect(x.sp@barcode, barcodes$barcode);
  x.sp = x.sp[match(barcode.shared, x.sp@barcode),];
  barcodes = barcodes[match(barcode.shared, barcodes$barcode),];
  x.sp@metaData = barcodes;
  x.sp
})
names(x.sp.ls) = sample.names;
x.sp = Reduce(snapRbind, x.sp.ls);
x.sp@metaData["sample"] = x.sp@sample;
x.sp
```

### Add cell-by-bin matrix
```{r}
x.sp = addBmatToSnap(x.sp, bin.size = 5000)
```
## Binarize matrix
Some items in the count matrix have abnormally high coverage perhaps due to the alignment errors. Therefore, we next remove top 0.1% items in the count matrix and then convert the remaining non-zero values to 1.
```{r}
x.sp = makeBinary(x.sp, mat="bmat")
x.sp
```

## Filter bins
Filter out bins overlapping w ENCODE blacklist

```{r}
black_list = read.table("~/annotations/hg38.blacklist.bed.gz")
black_list.gr = GRanges(
  black_list[,1], 
  IRanges(black_list[,2], black_list[,3])
);
idy = queryHits(
  findOverlaps(x.sp@feature, black_list.gr)
);
if(length(idy) > 0){
  x.sp = x.sp[,-idy, mat="bmat"];
};
x.sp
```

Exclude bad chromosomes
```{r}
chr.exclude = seqlevels(x.sp@feature)[grep("random|chrM", seqlevels(x.sp@feature))]
idy = grep(paste(chr.exclude, collapse="|"), x.sp@feature)
if(length(idy) > 0){
  x.sp = x.sp[,-idy, mat="bmat"]
}
x.sp
```

remove the top 5% bins that overlap with invariant features such as the house keeping gene promoters

```{r}
bin.cov = log10(Matrix::colSums(x.sp@bmat)+1)
# bin.cov = Matrix::colSums(x.sp@bmat)
hist(
  bin.cov[bin.cov > 0], 
  xlab="log10(bin cov)", 
  main="log10(Bin Cov)", 
  col="lightblue", 
  # xlim=c(0, 5),
  breaks=100
);
bin.cutoff = quantile(bin.cov[bin.cov > 0], 0.95)
idy = which(bin.cov <= bin.cutoff & bin.cov > 0)
x.sp = x.sp[, idy, mat="bmat"];
x.sp
```

Remove any cells of bin coverage less than 1,000. The rational behind this is that some cells may have high number of unique fragments but end up with low bin coverage after filtering. This step is optional but highly recommanded.
```{r}
idx = which(Matrix::rowSums(x.sp@bmat) > 1000);
x.sp = x.sp[idx,];
x.sp
```

## Dimensionality reduction
Uses diffusion map algorithm w sampling technique to make it fast.

```{r}
## Sample 100 cells as landmarks 
row.covs.dens <- density(
  x = x.sp@metaData[,"logUMI"], 
  bw = 'nrd', adjust = 1
)
sampling_prob <- 1 / (approx(x = row.covs.dens$x, y = row.covs.dens$y, xout = x.sp@metaData[,"logUMI"])$y + .Machine$double.eps);
set.seed(1)
idx.landmark.ds <- sort(sample(x = seq(nrow(x.sp)), size = 1000, prob = sampling_prob))

## Split between landmark and query cells
x.landmark.sp = x.sp[idx.landmark.ds,];
x.query.sp = x.sp[-idx.landmark.ds,];

## Run diffusion map on landmark
x.landmark.sp = runDiffusionMaps(
  obj= x.landmark.sp,
  input.mat="bmat", 
  num.eigs=50
);
x.landmark.sp@metaData$landmark = 1;

## Project query cells
x.query.sp = runDiffusionMapsExtension(
  obj1=x.landmark.sp, 
  obj2=x.query.sp,
  input.mat="bmat"
)
x.query.sp@metaData$landmark = 0;

## Combine 
x.sp = snapRbind(x.landmark.sp, x.query.sp);
x.sp = x.sp[order(x.sp@metaData[,"sample"])]; #IMPORTANT
```

To determine significant diffusion components:
> We use an ad hoc method by simply looking at a pairwise plot and select the number of eigen vectors that the scatter plot starts looking like a blob. In the below example, we choose the first 15 eigen vectors.

```{r}
plotDimReductPW(
    obj=x.sp, 
    eigs.dims=1:50,
    point.size=0.3,
    point.color="grey",
    point.shape=19,
    point.alpha=0.6,
    down.sample=5000,
    pdf.file.name=NULL, 
    pdf.height=7, 
    pdf.width=7
  );
```

## Clustering and visualization
```{r}
signif.dims = 1:16
x.sp = runKNN(
    obj=x.sp,
    eigs.dims=signif.dims,
    k=15
  );

x.sp=runCluster(
    obj=x.sp,
    tmp.folder=tempdir(),
    louvain.lib="leiden",
    seed.use=10,
    resolution=0.7
  );
```

Visualization
```{r, fig.height=5, fig.width=5}
x.sp = runViz(
 obj=x.sp, 
 tmp.folder=tempdir(),
 dims=2,
 eigs.dims=signif.dims, 
 method="umap",
 seed.use=10
)

plotViz(
    obj= x.sp,
    method="umap", 
    main="Cluster",
    point.color=x.sp@cluster, 
    point.size=0.2, 
    point.shape=19, 
    text.add=TRUE,
    text.size=1,
    text.color="black",
    down.sample=10000,
    legend.add=FALSE
  );

```


<!-- ## Outlier analysis -->
<!-- ```{r} -->
<!-- data.frame(x.sp@umap) %>% -->
<!--   mutate(cell=x.sp@barcode) %>% -->
<!--   # arrange(umap.1) -->
<!--   ggplot(aes(umap.1, umap.2)) + -->
<!--   geom_point(size=0.2) -->
<!-- ``` -->
<!-- ```{r, fig.height=5, fig.width=15} -->
<!-- outlier.ix.1 <- data.frame(x.sp@umap) %>% -->
<!--   mutate(cell=x.sp@barcode) %>% -->
<!--   rowid_to_column() %>% -->
<!--   dplyr::filter(umap.1 > 100) %>% -->
<!--   pull(rowid) -->
<!-- outlier.ix.2 <- data.frame(x.sp@umap) %>% -->
<!--   mutate(cell=x.sp@barcode) %>% -->
<!--   rowid_to_column() %>% -->
<!--   dplyr::filter(umap.2 < -50) %>% -->
<!--   pull(rowid) -->

<!-- smp.other <- sample(setdiff(1:length(x.sp@barcode), c(outlier.ix.1, outlier.ix.2)), 100) -->

<!-- long.outlier.bmat <- -->
<!--   x.sp@bmat[c(outlier.ix.1, outlier.ix.2, smp.other),] %>% -->
<!--   as.matrix() %>% -->
<!--   melt(value.name = "acc", varnames=c("barcode", "bin")) -->

<!-- # long.outlier.bmat %>% -->
<!-- #   # dplyr::filter(barcode=='AACAAAGGTAGACGCA-1') %>% -->
<!-- #   mutate(big.bins=cut(bin,breaks = 10000)) %>% -->
<!-- #   group_by(barcode) %>% -->
<!-- #   arrange(bin) %>% -->
<!-- #   mutate(runmean=rollmean(acc,k = 10000)[1:n()]) %>% -->
<!-- #   ungroup() %>% -->
<!-- #   group_by(barcode, big.bins) %>% -->
<!-- #   summarise(bin.mean=mean(runmean)) %>% -->
<!-- #   mutate(big.bins=as.numeric(big.bins)) %>% -->
<!-- #   mutate(outlier=ifelse(barcode %in% x.sp@barcode[outlier.ix], TRUE, FALSE)) %>% -->
<!-- #   ggplot(aes( big.bins, barcode, fill=bin.mean)) + -->
<!-- #   geom_tile() + -->
<!-- #   scale_fill_viridis_c() + -->
<!-- #   facet_wrap(outlier~., scales="free_y", ncol=1, nrow=2) -->
<!-- #   ungroup() %>% -->
<!-- #   ggplot(aes(bin, runmean)) + -->
<!-- #   facet_wrap(big.bins~., scale="free_x", ncol=2) + -->
<!-- #   geom_line(aes(group=barcode, color=outlier), size=0.5, alpha=0.5) -->
<!-- #    -->
<!-- ``` -->

<!-- ```{r} -->
<!-- long.outlier.bmat %>% -->
<!--   group_by(barcode) %>% -->
<!--   summarise(acc=sum(acc)) %>% -->
<!--   mutate(outlier=case_when(barcode %in% x.sp@barcode[outlier.ix.1] ~  "out1",  -->
<!--                            barcode %in% x.sp@barcode[outlier.ix.2] ~ "out2", -->
<!--                            FALSE ~ 'other')) %>% -->
<!--   # mutate(outlier=ifelse(barcode %in% x.sp@barcode[outlier.ix.2], "out2", FALSE)) %>% -->
<!--   ggplot(aes(outlier, acc)) + -->
<!--   ggbeeswarm::geom_quasirandom() -->
<!-- ``` -->


## Make gene matrix
```{r}
transcripts.gr = rtracklayer::import("~/Homo_sapiens.GRCh38.93.filtered.gtf")
colnames(transcripts.gr@elementMetadata) <- str_replace(colnames(transcripts.gr@elementMetadata), "gene_name", "name")

genes.gr <- unlist(range(split(transcripts.gr, ~ name)))  ## From transcripts to genes
genes.gr$name <- names(genes.gr)

if (GenomeInfoDb::seqlevelsStyle(genes.gr) != GenomeInfoDb::seqlevelsStyle(x.sp@feature) ) {
  GenomeInfoDb::seqlevelsStyle(genes.gr) <- GenomeInfoDb::seqlevelsStyle(x.sp@feature)
}

x.sp = createGmatFromMat(
    obj=x.sp, 
    input.mat="bmat",
    genes=genes.gr,
    do.par=TRUE,
    num.cores=10
  )

saveRDS(x.sp, file = "~/my_data/cellranger-atac110_count_30439_WSSS8038360_GRCh38-1_1_0.snapATAC.RDS")
x.sp <- readRDS( "~/my_data/cellranger-atac110_count_30439_WSSS8038360_GRCh38-1_1_0.snapATAC.RDS")
```

Repeat visualization on gmat
```{r}
## Make suerat object
f74.seu <- snapToSeurat(
    obj=x.sp, 
    eigs.dims=1:20, 
    norm=TRUE,
    scale=TRUE
    )

## Save UMAP based on SnapATAC processing
f74.seu <- RunUMAP(f74.seu, reduction = "SnapATAC", dims=signif.dims, reduction.name = "umap.snap")


f74.seu <- FindVariableFeatures(f74.seu)
f74.seu <- RunPCA(f74.seu, dims=1:50)
f74.seu <- RunUMAP(f74.seu, reduction = "pca", dims=1:50, verbose=FALSE) 

Seurat::Idents(f74.seu) <- x.sp@cluster

ggpubr::ggarrange(
  DimPlot(f74.seu, reduction = "umap") + ggtitle("gmat"),
  DimPlot(f74.seu, reduction = "umap.snap") + ggtitle("bmat - snap") 
  )

```


```{r}
plotly::ggplotly(DimPlot(f74.seu, reduction = "umap", group.by = NULL) + ggtitle("gmat"))
```

```{r, fig.height=15, fig.width=15}
FeaturePlot(f74.seu, features = c('PTPRC','CD4','CD8A','CD8B','CD79A','FOXN1','EPCAM','PDGFRA','GNG4', 'FOXP3','RAG1','RAG2','NKG7','CCR7'), reduction = "umap.snap")
```

```{r, fig.height=15, fig.width=15}
DimHeatmap(f74.seu, dims = 1:6, balanced = TRUE,ncol = 1)
```

### Differential analysis 
<!-- Snap ATAC way -->
<!-- ```{r} -->
<!-- cluster = 1 -->
<!-- DARs= findDAR( -->
<!--     obj=x.sp, -->
<!--     input.mat="gmat", -->
<!--     cluster.pos=cluster, -->
<!--     # cluster.neg = 5, -->
<!--     cluster.neg.method="random", -->
<!--     test.method="exactTest", -->
<!--     bcv=0.4, #0.4 for human, 0.1 for mouse -->
<!--     seed.use=42 -->
<!--   ); -->


<!-- gmat.cl <- x.sp@gmat[which(x.sp@cluster==cluster),] -->
<!-- gmat.neg <- x.sp@gmat[which(x.sp@cluster!=cluster),] -->

<!-- hist(as.matrix(gmat.cl), breaks=50) -->


<!-- # DARs %>% -->
<!--   # mutate(gene=colnames(x.sp@gmat)) %>% -->
<!--   # arrange() %>% -->
<!--   # filter(PValue < 0.05) -->

<!-- hist(DARs$PValue, breaks = 20) -->

<!-- DARs$FDR = p.adjust(DARs$PValue, method="BH") -->
<!-- idy = which(DARs$FDR < 5e-2 & DARs$logFC > 0) -->
<!-- plot(DARs$logCPM, DARs$logFC,  -->
<!--     pch=19, cex=0.1, col="grey",  -->
<!--     ylab="logFC", xlab="logCPM", -->
<!--     main=paste("Cluster", cluster) -->
<!--   ) -->
<!-- points(DARs$logCPM[idy],  -->
<!--     DARs$logFC[idy],  -->
<!--     pch=19,  -->
<!--     cex=0.5,  -->
<!--     col="red" -->
<!--   ) -->

<!-- ``` -->
```{r}
DAGs_cl <- FindAllMarkers(f74.seu, min.pct = 0.2, logfc.threshold = 0.3)
DAGs_cl <- 
  DAGs_cl %>%
  mutate(positive=avg_logFC > 0)
```
```{r, fig.height=14, fig.width=9}
top10_markers <- DAGs_cl %>%
  filter(positive) %>%
  group_by(cluster) %>%
  arrange(p_val_adj) %>%
  top_n(10) %>%
  pull(gene) %>%
  unique()

top10_markers <- DAGs_cl %>%
  filter(positive) %>%
  group_by(cluster) %>%
  # arrange(p_val_adj) %>%
  top_n(10,wt = - p_val_adj) %>%
  pull(gene)

DotPlot(f74.seu, features = top10_markers) +
  coord_flip()
```

Expression of markers in RNA data

```{r, fig.width=12, fig.height=16}
plot_marker_ATACvsRNA <- function(marker){
    ggpubr::ggarrange(
      FeaturePlot(f74.seu, feature=marker, reduction="umap.snap") + ggtitle("ATAC"), FeaturePlot(f74.rna.seu, feature=marker) + ggtitle("RNA")
    ) %>% annotate_figure(top=marker)
  }

plot_cluster_marker_ATACvsRNA <- function(cl){
  top10_cluster <-
    DAGs_cl %>%
    filter(positive) %>%
    filter(cluster==cl) %>%
    filter(gene %in% rownames(f74.rna.seu)) %>%
    top_n(10,wt = - p_val_adj) %>%
    pull(gene)
  ggarrange(plotlist = map(top10_cluster, ~ plot_marker_ATACvsRNA(.x)), ncol=2, nrow=5) %>% 
    annotate_figure(fig.lab=paste("Cluster", cl), fig.lab.size = 20, fig.lab.face = "bold")
  }

map(1:10, ~ plot_cluster_marker_ATACvsRNA(.x))
```

## Differential analysis only between the putative T-cell clusters
```{r, fig.width=8, fig.height=15}
t_clusters <- c("1", '2', '3', '4', '5')
DAGs_tcells <- map(t_clusters, ~ FindMarkers(f74.seu, ident.1 = .x, ident.2 = setdiff(t_clusters,.x), min.pct = 0.2, logfc.threshold = 0.3))

top20_tcells_markers <- imap(DAGs_tcells, ~ .x %>% rownames_to_column() %>% filter( avg_logFC > 0) %>% mutate(cluster=.y)) %>%
  purrr::reduce(bind_rows) %>%
  # filter(positive) %>%
  group_by(cluster) %>%
  top_n(20,wt = - p_val_adj) %>%
  pull(rowname)


DotPlot(f74.seu, features = top20_tcells_markers) +
  coord_flip() +
  ggtitle("T-cell subtypes markers (DA between clusters 1-5)")
```

Only between cluster 1 and 2
```{r}
DAGs_tcells_12 <- FindMarkers(f74.seu, ident.1 = '1', ident.2 = '2', min.pct = 0.2, logfc.threshold = 0.3)

top20_cl12_markers <- DAGs_tcells_12 %>% 
  rownames_to_column() %>% 
  filter( avg_logFC > 0) %>% 
  top_n(20,wt = - p_val_adj) %>%
  pull(rowname)

DotPlot(f74.seu, features = top20_cl12_markers) +
  coord_flip() +
  ggtitle("DA between clusters 1-2")
```



```{r}
plotly::ggplotly(DimPlot(f74.rna.seu, group.by = "annotation"))
```


## Embedding on most variable genes in ATAC (not in gene expression)
```{r}
DefaultAssay(f74.seu) <- "ACTIVITY"
f74.seu.hvgatac <- FindVariableFeatures(f74.seu)
f74.seu.hvgatac <- RunPCA(f74.seu.hvgatac, dims=1:30)
f74.seu.hvgatac <- RunUMAP(f74.seu.hvgatac, reduction = "pca", dims=1:30, verbose=FALSE) 

f74.seu.hvgatac <- AddMetaData(f74.seu.hvgatac, x.sp@cluster, col.name='SnapATAC_cluster')
DimPlot(f74.seu.hvgatac, reduction = "umap", group.by = "SnapATAC_cluster") 
```

## Save markers
```{r}
DAGs_cl %>% filter(positive) %>% write_csv("~/my_data/integrated_thymus/F74_ATAC_clusterMarkers_allVSall_20191104.csv")
imap(DAGs_tcells, ~ .x %>% 
  rownames_to_column("gene") %>%
  filter(avg_logFC > 0) %>%
  mutate(cluster=.y)) %>%
  purrr::reduce(bind_rows) %>%
  write_csv("~/my_data/integrated_thymus/F74_ATAC_clusterMarkers_1to5VS1to5_20191104.csv")

DAGs_tcells_12 %>% 
  rownames_to_column("gene") %>%
  write_csv("~/my_data/integrated_thymus/F74_ATAC_clusterMarkers_1VS2_20191104.csv")
```



## Thoughts

- There seems to be a correspondance for gene expression and accessibility in many "accessibility markers" (see Cluster 6) --> maybe integration would work better if I don't take only the most variable features in the RNA data. 



